Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/45672
This PR merges all quantization mode and will only expose the following top level functions:
```
prepare_fx
prepare_qat_fx
convert_fx
```
Test Plan:
Imported from OSS
Imported from OSS
Reviewed By: z-a-f
Differential Revision: D24053439
fbshipit-source-id: 03d545e26a36bc22a73349061b751eeb35171e64
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/45292
This PR merges all quantization mode and will only expose the following top level functions:
```
prepare_fx
prepare_qat_fx
convert_fx
```
Test Plan: Imported from OSS
Reviewed By: vkuzo
Differential Revision: D23913105
fbshipit-source-id: 4e335286d6de225839daf51d1df54322d52d68e5
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/44856
Support following format of qconfig_dict
```python
qconfig_dict = {
# optional, global config
"": qconfig?,
# optional, used for module and function types
# could also be split into module_types and function_types if we prefer
"object_type": [
(nn.Conv2d, qconfig?),
(F.add, qconfig?),
...,
],
# optional, used for module names
"module_name": [
("foo.bar", qconfig?)
...,
],
# optional, matched in order, first match takes precedence
"module_name_regex": [
("foo.*bar.*conv[0-9]+", qconfig?)
...,
]
# priority (in increasing order): global, object_type, module_name_regex, module_name
# qconfig == None means fusion and quantization should be skipped for anything
# matching the rule
}
```
Test Plan: Imported from OSS
Reviewed By: vkuzo
Differential Revision: D23751304
fbshipit-source-id: 5b98f4f823502b12ae2150c93019c7b229c49c50
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/43910
Adds a debug function to get a representation of all nodes in the
graph, such as
```
name op target args kwargs
x plchdr x () {}
linear_weight gt_prm linear.weight () {}
add_1 cl_fun <bi_fun add> (x, linear_weight) {}
linear_1 cl_mod linear (add_1,) {}
relu_1 cl_meth relu (linear_1,) {}
sum_1 cl_fun <bi_meth sum> (relu_1,) {'dim': -1}
topk_1 cl_fun <bi_meth topk> (sum_1, 3) {}
```
using only Python STL. This is useful for printing internal state of
graphs when working on FX code.
Has some on-by-default logic to shorten things so that node reprs for
toy models and unit tests fit into 80 chars.
Flexible on function name and location, I care more that this is
accessible from both inside PT as well as from debug scripts which
are not checked in.
Test Plan:
see
https://gist.github.com/vkuzo/ed0a50e5d6dc7442668b03bb417bd603 for
example usage
Imported from OSS
Reviewed By: jerryzh168
Differential Revision: D23435029
fbshipit-source-id: 1a2df797156a19cedd705e9e700ba7098b5a1376
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/43901
Add similar APIs like eager and graph mode on torchscript
- fuse_fx
- quantize_fx (for both post training static and qat)
- quantize_dynamic_fx (for post training dynamic)
- prepare_fx (for both post training static and qat)
- prepare_dynamic_fx (for post training dynamic)
- convert_fx (for all modes)
Test Plan:
Imported from OSS
Imported from OSS
Reviewed By: vkuzo
Differential Revision: D23432430
fbshipit-source-id: fc99eb75cbecd6ee7a3aa6c8ec71cd499ff7e3c1
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/43581
Add similar APIs like eager and graph mode on torchscript
- fuse_fx
- quantize_fx (for both post training static and qat)
- quantize_dynamic_fx (for post training dynamic)
- prepare_fx (for both post training static and qat)
- prepare_dynamic_fx (for post training dynamic)
- convert_fx (for all modes)
Test Plan: Imported from OSS
Reviewed By: vkuzo
Differential Revision: D23385091
fbshipit-source-id: b789e54e1a0f3af6b026fd568281984e253e0433
